You have implemented a Snowpipe using auto-ingest to load data from an AWS S3 bucket. The pipe is configured to load data into a table with a 'DATE column ('TRANSACTION DATE'). The data files in S3 contain a date field in the format 'YYYYMMDD'. Occasionally, you observe data loading failures in Snowpipe with the error message indicating an issue converting the string to a date. The 'FILE FORMAT' definition includes 'DATE FORMAT = 'YYYYMMDD''. Furthermore, you are also noticing that after a while, some files are not being ingested even though they are present in the S3 bucket. How to effectively diagnose and resolve these issues?

• A. The error could be due to invalid characters in the source data files. Implement data cleansing steps to remove invalid characters from the date fields before uploading to S3. For files not being ingested, check S3 event notifications for missing or failed events.
• B. Verify that the 'DATE FORMAT is correct and that all files consistently adhere to this format. Check for corrupted files in S3 that may be preventing Snowpipe from processing subsequent files. Additionally, review the Snowpipe error notifications in Snowflake to identify the root cause of ingestion failures. Use 'SYSTEM$PIPE to troubleshoot the files not ingested
• C. The issue may arise if the time zone of the Snowflake account does not match the time zone of your data in AWS S3. Try setting the 'TIMEZONE parameter in the FILE FORMAT definition. For files that are not being ingested, manually refresh the Snowpipe with 'ALTER PIPE ... REFRESH'.
• D. Snowflake's auto-ingest feature has limitations and may not be suitable for inconsistent data formats. Consider using the Snowpipe REST API to implement custom error handling and data validation logic. Monitor the Snowflake event queue to ensure events are being received.
• E. The 'DATE FORMAT parameter is case-sensitive. Ensure it matches the case of the incoming data. Also, check the 'VALIDATION MODE and ERROR parameters to ensure error handling is appropriately configured for files with date format errors. For the files that are not ingested use 'SYSTEM$PIPE to find the cause of the issue.

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You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:

• A. Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.
• B. Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.
• C. Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.
• D. Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.
• E. Using followed by is an efficient approach to calculate unique users per page per day.

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A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

• A. Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.
• B. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
• C. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
• D. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
• E. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.

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You are tasked with implementing row-level security (RLS) on a 'SALES' table to restrict access based on the 'REGION' column. Users with the 'NORTH REGION ROLE should only see data where 'REGION = 'NORTH". You've created a row access policy named north_region_policy'. After applying the policy to the 'SALES table, users with the 'NORTH REGION ROLE are still seeing all rows.
Which of the following is the MOST likely reason for this and how can it be corrected?

• A. The ' does not have the USAGE privilege on the database and schema containing the 'SALES' table. Grant the USAGE privilege to the role.
• B. The policy function within is not using the correct context function to determine the user's role. It should use 'CURRENT ROLE()' instead of 'CURRENT_USER()'
• C. The user has not logged out and back in since the role was granted to them. Force the user to re-authenticate.
• D. The policy needs to be explicitly refreshed. Execute 'REFRESH ROW ACCESS POLICY north_region_policy ON SALES;'
• E. The is not enabled. Execute 'ALTER ROW ACCESS POLICY ON SALES SET ENABLED = TRUE;'

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A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table 'SALES FACT (100 billion rows) and several dimension tables, including 'CUSTOMER DIM', 'PRODUCT DIM', and 'DATE DIM'. The queries are run every morning and are essential for business decision-making. The team has identified that the 'SALES FACT table's primary key is 'SALE ID, but the queries frequently filter and join on 'CUSTOMER and 'PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

• A. Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.
• B. Create materialized views that pre-aggregate the sales data based on 'CUSTOMER ID', 'PRODUCT ID, and 'DATE ID, then enable query acceleration on the virtual warehouse.
• C. Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.
• D. Enable clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.
• E. Enable search optimization on the columns 'CUSTOMER ID' and 'PRODUCT ID of the 'SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.

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